Seeking Democracy Inside, and Outside, of Education: Re-conceptualizing Perceptions and Experiences Related to Democracy and Education
Bibliographic record
Abstract
This conceptual article underscores the importance of critical engagement in and through education with a view to enhancing education for democracy (EfD). As a centerpiece to illustrating this connection, we refer to our research project, which engages international actors through an analysis of the perceptions, experiences and perspectives of education students, educators and others in relation to EfD. The article presents the Thick-Thin Spectrum of EfD and a Spectrum for Critical Engagement for EfD to re(present) the problematic of political engagement and literacy on the part of teacher-education students. The findings of our study highlight a necessity for education to be connected and linked to deliberative and participatory democracy in a critical manner in order to create positive, progressive, and transformative educational opportunities, especially in relation to inequitable power relations and social justice. In sum, we seek to re(conceptualize) the meaning of democracy within, and for, education while making the linkage with the lived experience of future educators and others involved in formal education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.078 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".